Retracted: Artificial intelligence for classification and regression tree based feature selection method for network intrusion detection system in various telecommunication technologies

Abstract Now a days, secure data communication over computer network system is a major issue in which impact of feature reduction plays a vital role to secure network by early detection of intrusion. It not only keeps a deep impact on the performance of existing Intrusion Detection System (IDS) algorithms but also affects the computational complexity. Although lots of techniques have been offered for feature reduction by researchers and they have their own perks and quirks, but still they are several flows. To manipulate the same dataset for different classifiers and to select different number of features for the detection of attacks are not only having too much computational cost but also time consuming. The experiments have been carried out using “Python” programming language based library “Scikit‐Learn” software on “Kddcup99” dataset from UCI machine learning repository as a test bed. In this article a classification and regression trees (CART) based feature selection algorithm has been proposed which offers optimum set of features. Further optimum set of features has been offered by our proposed work passed over various classifiers for training and testing to establish network intrusion detection system (NIDS). We have compared the performance accuracy of various existing machine learning (ML) based classification algorithms and obtained higher performance accuracy with lower computational cost. The proposed algorithm having optimum time complexity and accuracy in designing of IDS.